小编给大家分享一下怎么在Ubuntu 18.04服务器上安装TensorFlow，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！
我们假设使用64位的操作系统，显卡为GeForce 740m。SSH登录到服务器，更新和升级：
apt update -y apt upgrade –y
运行这个命令来安装Python库：
sudo apt install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy python-six python3-six build-essential python-pip python3-pip python-virtualenv swig python-wheel python3-wheel libcurl3-dev libcupti-dev
继续运行
sudo apt install libcurl4-openssl-dev
通过运行，我们可以看到安装的显卡硬件：
sudo lshw -C display | grep product
我们需要安装Nvidia驱动程序。我们可以检查SSH上的图形驱动程序：
nvidia-smi
这是Ubuntu的PPA，浏览一下:
https://launchpad.net/~graphics-drivers/+archive/ubuntu/ppa
nvidia-graphics-drivers-396是最新的，但可能没有太多测试。我们可以添加 nvidia-graphics-drivers-390 PPA 并安装该应用程序。
sudo add-apt-repository ppa:graphics-drivers/ppa sudo apt update sudo apt upgrade ubuntu-drivers devices sudo ubuntu-drivers autoinstall
如果有意外情况，autoinstall不起作用，则运行：
sudo apt install nvidia-390
现在，再次运行命令：
nvidia-smi
您将得到一个有用的输出。我们应该保持住这个版本停止升级。
sudo apt-mark hold nvidia-driver-390
安装 Linux—headers :
sudo apt install linux-headers-$(uname -r)
为了后续步骤正常进行，我们需要 gcc, g++ 等等：
apt install gcc g++ gcc-6 g++-6 gcc-4.8 g++-4.8 # if gcc-4.8 package not found run # sudo add-apt-repository ppa:ubuntu-toolchain-r/test # sudo apt update # sudo apt install gcc-4.8 g++-4.8
现在我们必须安装CUDA工具包：
apt install nvidia-cuda-toolkit libcupti-dev
重启
sudo reboot
安装CUDA工具包:
https://developer.nvidia.com/cuda-toolkit
运行：
cd Downloads/ sudo sh cuda_9.0.176_384.81_linux.run --override --silent –toolkit
接下来，您需要安装CUDNN，NCCL。您需要按照PyTorch老方法，使用Nvdia帐户登录，这很简单。您将获得链接：cuDNN v7.1.x Library for Linux。您需要下载deb文件，并将FTP上传到服务器。URL是：
https://developer.nvidia.com/rdp/cudnn-download
https://developer.nvidia.com/nccl
找到已安装CUDA的目录。它正在将文件复制到/usr/local/cuda/。将上述内容移到安装CUDA的目录中并运行这些操作（注意版本编号的目录，以下是格式示例）：
tar -xzvf cudnn-9.0-linux-x64-v7.1.tgz sudo cp cuda/include/cudnn.h /usr/local/cuda/include sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
以上将节省空间，并避免apt警告。打开配置文件，如.bashrc：
nano ~/.bashrc
添加这些：
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" export CUDA_HOME=/usr/local/cuda
重新加载：
source ~/.bashrc sudo ldconfig echo $CUDA_HOME
安装Bazel：
sudo apt install curl echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add - sudo apt update -y sudo apt upgrade -y sudo apt install bazel sudo apt upgrade bazel pip install keras
查看Nvidia版本：
cd ~ git clone https://github.com/tensorflow/tensorflow cd ~/tensorflow # check current revision number from browser git checkout r1.11 cd ~/tensorflow
通过运行创建配置文件：
./configure
您将得到这样的输出：
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3 Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: Y Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: N Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: N Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: N Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: N Do you wish to build TensorFlow with XLA JIT support? [y/N]: N Do you wish to build TensorFlow with GDR support? [y/N]: N Do you wish to build TensorFlow with VERBS support? [y/N]: N Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: N Do you wish to build TensorFlow with CUDA support? [y/N]: Y Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: 9.0 Please specify the location where CUDA 9.1 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.1 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda Do you wish to build TensorFlow with TensorRT support? [y/N]: N Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 5.0] 3.0 Do you want to use clang as CUDA compiler? [y/N]: N Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: /usr/bin/gcc-4.8 Do you wish to build TensorFlow with MPI support? [y/N]: N Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: -march=native Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:N
构建TensorFlow :
最后的步骤：
bazel-bin/tensorflow/tools/pip_package/build_pip_package tensorflow_pkg cd tensorflow_pkg/ sudo pip3 install tensorflow-.whl
通过切换到另一个目录并运行python来检查您的构建是否正常工作：
import tensorflow as tf
hello = tf.constant('Hello World!')
sess = tf.Session()
print(sess.run(hello))您将得到Hello World!输出。TensorFlow有以下型号：
https://github.com/tensorflow/models
您可以运行：
git clone https://github.com/tensorflow/models.git cd models/tutorials/image/imagenet python classify_image.py
这是一些基本设置和测试。
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